LSTM, or Long Short-Term Memory, is a type of recurrent neural network (RNN) architecture designed to effectively capture long-range dependencies in sequential data. It addresses the vanishing gradient problem common in traditional RNNs by utilizing special gating mechanisms that control the flow of information, allowing it to retain and forget information over long periods. This feature makes LSTMs particularly powerful for tasks like language modeling and text generation, where understanding context over several time steps is crucial.
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